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Record W2133565714 · doi:10.1109/i2mtc.2012.6229719

Low-cost, rapid prototyping of IMU and pressure monitoring system using an open source hardware design

2012· article· en· W2133565714 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSensor Technology and Measurement Systems
Canadian institutionsCarleton University
Fundersnot available
KeywordsInertial measurement unitSoftware prototypingEmbedded systemOpen sourceComputer scienceOpen source hardwareRapid prototypingComputer hardwareEngineeringSoftwareOperating systemSoftware developmentArtificial intelligence

Abstract

fetched live from OpenAlex

Open source hardware is a type of hardware where the schematics and designs are made unrestricted and available to all. They are often accompanied by open source software. This can bring reliability, ease of debugging, and modular development for rapid prototyping using pre-written libraries. Merits of using open source hardware are discussed and then applied to a portable sensor system based on the open hardware Arduino-derived Jeenode microcontroller board. The system uses an inertial measurement unit (IMU) and seamless integration of other sensors, including a piezo-resistive pressure sensor. It is shown that open source hardware can help to increase rapid development, reduce costs, and encourage further development.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.872
Threshold uncertainty score0.491

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.127
GPT teacher head0.299
Teacher spread0.173 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations27
Published2012
Admission routes1
Has abstractyes

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